Date of Award

Spring 2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Transportation - (Ph.D.)

Department

Civil and Environmental Engineering

First Advisor

Marhaba, Taha F.

Second Advisor

Ravindra, N. M.

Third Advisor

Daniel, Janice Rhoda

Fourth Advisor

Bladikas, Athanassios K.

Fifth Advisor

Yang, Jian

Abstract

Mixed-use developments and transit-oriented developments are becoming very common in urban areas in an effort to reduce sprawl. Numerous studies have shown that such programs would not be successful unless the mix of land uses and sizes is well-balanced and integrated with the surrounding neighborhood. Developers often ignore this aspect in favor of immediate financial gain and do not realize that there are sustainable financial benefits in land use optimization. In addition, professionals often work with limited logistics, resources, and technical knowledge and therefore struggle in setting goals and suggesting land uses that have less auto dependency based on travel demand characteristics.

The current traffic impact assessment methodology (part of the environmental review process for approval of a project) is one-dimensional. It does not consider land use optimization based on the surrounding neighborhood characteristics that have a significant effect in reducing vehicular traffic. These surrounding neighborhood characteristics are often grouped into categories reflecting the “D’s of development”—Density, Diversity, Design, and Distance to transit—and would have significant benefits in minimizing auto dependence.

The objectives of this research are to first develop a methodology to optimize the mix of land uses and sizes to minimize the number vehicular trips and maximize the person trips using a case study of a mixed-use development. This will help to further understand the travel demand and parking behavior. Secondly, this research will use the travel demand characteristics from other approved mixed-use developments from various boroughs of New York City with diverse neighborhood characteristics to validate the land use optimization methodology. The third and ultimate objective of this research is to develop a model that is practical and implementable on a regional level to optimize the mix of land uses and sizes based on localized travel behavior patterns and neighborhood characteristics to minimize vehicle trips. In this study, a genetic algorithm has been developed and tested on one development to demonstrate its application (objective 1) and is subsequently applied to additional developments (objective 2). A stepwise regression analysis is then performed to develop equations for the optimal number of vehicle trips as well as the percent split of individual land use types within a development, all based on the surrounding neighborhood characteristics (objective 3). The research results in a series of equations that can be used to optimize a development’s mix of land uses and sizes by minimizing vehicular trips and maximizing person trips. Although the equations vary from city to city, the methodology is adaptable enough such that planning agencies can generate their own equations for their own region and engineers can then use them to forecast trips

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